-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies ...-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.展开更多
由于气温突变点的影响,负荷序列存在门限效应,导致传统线性时间序列模型的负荷预测效果较差。将气温突变点作为门限,建立了以气温为协变量的门限自回归移动平均(threshold autoregressive moving average with exogenous variable,TARM...由于气温突变点的影响,负荷序列存在门限效应,导致传统线性时间序列模型的负荷预测效果较差。将气温突变点作为门限,建立了以气温为协变量的门限自回归移动平均(threshold autoregressive moving average with exogenous variable,TARMAX)模型,提高了预测精度。首先,应用马尔科夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)方法对气温突变点进行搜寻得到模型参数。然后,采用随机搜索变量的方法快速选择出最优模型,有效降低选择时间序列模型的计算量。最后,对不同季节下的居民日用电负荷进行预测。实例表明,与线性时间序列模型、长短期记忆网络(long short-term memory network,LSTM)和多层感知机(multilayer perceptron,MLP)相比,TARMAX模型提高了电力负荷的预测精度。展开更多
文摘-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.
文摘由于气温突变点的影响,负荷序列存在门限效应,导致传统线性时间序列模型的负荷预测效果较差。将气温突变点作为门限,建立了以气温为协变量的门限自回归移动平均(threshold autoregressive moving average with exogenous variable,TARMAX)模型,提高了预测精度。首先,应用马尔科夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)方法对气温突变点进行搜寻得到模型参数。然后,采用随机搜索变量的方法快速选择出最优模型,有效降低选择时间序列模型的计算量。最后,对不同季节下的居民日用电负荷进行预测。实例表明,与线性时间序列模型、长短期记忆网络(long short-term memory network,LSTM)和多层感知机(multilayer perceptron,MLP)相比,TARMAX模型提高了电力负荷的预测精度。
